shadow-assistant-data / ROADMAP.md
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SHADOW Assistant roadmap

Every step from data to release. Updated 2026-10-31.

Overall: 16 of 46 steps done ██████████░░░░░░░░░░░░░░░░░░░░ 35%

phase progress done
1. Data ████████████████████ 9 / 9
2. Proof and speed █████████████████░░░ 5 / 6
3. Main pretraining ░░░░░░░░░░░░░░░░░░░░ 0 / 5
4. Base benchmarks ░░░░░░░░░░░░░░░░░░░░ 0 / 6
5. Tuning (SFT) ░░░░░░░░░░░░░░░░░░░░ 0 / 4
6. Final benchmarks ░░░░░░░░░░░░░░░░░░░░ 0 / 2
7. Runtimes and ports ████░░░░░░░░░░░░░░░░ 2 / 9
8. Release ░░░░░░░░░░░░░░░░░░░░ 0 / 5

Now

  • ⏭️ next: Main-run trainer (window stages to 32k, anneal mix, skip lists, multi-GPU, streaming, resume)
  • ⏳ waiting: Compute for the main run (4x RTX PRO 6000, about 9 days)

1. Data

status step details
✅ done Text pretraining data 286B tokens, 15 sources, Australian 34B
✅ done Pictures and video about 9.5B picture tokens, 256,874 ALA photos
✅ done Audio about 37,700 hours incl. WavCaps
✅ done Anneal maths complete about 10.7B tokens
✅ done Tuning data text 7.4M, vision 1.74M, audio 1.34M, Kimi 28,992
✅ done Final unigram prior 285.7B tokens, 22,394 shards
✅ done Benchmark skip lists HellaSwag (3,299 videos), ESC-50/UrbanSound8K/VGGSound clips
✅ done Pending sources closed Trove declined; data final
✅ done Public data card README, GUIDE, AGENTS, decontamination lists

2. Proof and speed

status step details
✅ done Archive proof int4 KV, word-match search, best block last
✅ done Full-size test run 135M + 4B Engram, all senses, 0.85B tokens, passed
✅ done Fetch on the trained model fact recall 9% to 63% of digits
✅ done Training speed kernels fused GRU, fused readout, compile: 7.2x
✅ done CPU speed up to 128k 551 tok/s empty, 141 at 128k
⬜ to do Training speed at 32k on the main-run GPUs

3. Main pretraining

status step details
⏭️ next Main-run trainer window stages to 32k, anneal mix, skip lists, multi-GPU, streaming, resume
⏳ waiting Compute for the main run 4x RTX PRO 6000, about 9 days
⬜ to do Text decontamination scan 13-grams vs 9 benchmarks over all 286B tokens
⬜ to do Pretraining, 2k to 12k one pass of the corpus
⬜ to do 32k stage and anneal 40% long documents, 60% maths, code, best text

4. Base benchmarks

status step details
⬜ to do Evaluation harness runs every benchmark on each checkpoint
⬜ to do Quality for the size HellaSwag, ARC, PIQA, ArithMark, MMLU, GSM8K, code
⬜ to do Window: 32k to 128k needle in a haystack, RULER, YaRN to 128k
⬜ to do Memory beyond the window BABILong, LongMemEval, own archive test
⬜ to do Pictures and video MMMU, DocVQA, ChartQA, Video-MME, COCO
⬜ to do Audio LibriSpeech, AudioCaps, Clotho, MusicCaps, MMAU

5. Tuning (SFT)

status step details
⬜ to do SFT trainer chat template, loss on answers, Engram frozen
⬜ to do Text SFT 7.1M conversations
⬜ to do Media SFT pictures, video, sound and text together (SmolVLM2 mix)
⬜ to do Kimi SFT Australian life, identity, files, time, creative

6. Final benchmarks

status step details
⬜ to do All benchmarks again on the tuned model, plus IFEval
⬜ to do Real-use demos chat, photos, documents, sound, memory

7. Runtimes and ports

status step details
✅ done CPU kernels at real size ternary, int4 attention, RNN, Engram, eye
✅ done Check which runtimes accept new models llama.cpp takes new models (fork first, then pull requests); ONNX via a custom-op package; OpenVINO via optimum-intel
⬜ to do C++ runtime with real weights every feature, the speed headline
⬜ to do Hugging Face model code safetensors + modelling code; unblocks every converter
⬜ to do llama.cpp fork, then pull requests CPU text body first (TQ2_0, q4_0 KV), then GRU op, Engram, eye and ear; also reaches Ollama
⬜ to do MLX (Apple) single-file model, BitNet kernel exists
⬜ to do ONNX custom-op package stateful RNN, host code for Engram and archive
⬜ to do OpenVINO issue first with optimum-intel; Intel CPUs and GPUs
⬜ to do Optional GPU path for eye and ear about 20x faster sound encoding

8. Release

status step details
⬜ to do Model on Hugging Face weights, model card, CloudRift credit
⬜ to do Public code repository runtime and docs, one clean history
⬜ to do Open SLM Leaderboard entry sub-150M class
⬜ to do Papers paper 2: one space for all senses; index and archive
⬜ to do Announce after measured results

Status: ✅ done, 🔄 running, ⏭️ next, ⬜ to do, ⏳ waiting.